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<div class="item">Reduced hallucinations - RAG allows enormous, static LLMs with outdated training data to access new or specialized information to respond to user queries. This greatly reduces hallucinations by plugging the holes in the foundation model’s knowledge and giving it the context to formulate accurate responses.</div>
<div class="item">Up-to-date information - RAG overcomes the time cutoff of training data by giving the model access to current or real-time information about events and topics that occurred after the model training ended. This also reduces hallucinations and increases the accuracy and relevance of responses.</div>
<div class="item">Domain-specific knowledge - RAG is an effective and efficient way to augment foundation models with domain-specific data. Vector databases can be built at scale and at a relatively low cost since they do not require labeled datasets or SMEs. This makes RAG the best available method for model specialization to date, as compared with proprietary model building, fine-tuning and prompt engineering.</div>
<div class="item">Easy updates - RAG frameworks bypass the need for costly, time-intensive retraining and updating of foundation models. Databases can be updated simply by adding fresh documents (e.g., regarding new products, procedures or policies) or organically by accessing the internet.</div>
<div class="item">Source citations - RAG gives much-needed visibility into the sources of generative AI responses—any response that references external data provides source citations, allowing for direct verification and fact-checking.</div>
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